The Pre-FUFP algorithm for incremental mining

Chun Wei Lin, Tzung Pei Hong, Wen-Hsiang Lu

Research output: Contribution to journalArticlepeer-review

82 Citations (Scopus)

Abstract

The frequent pattern tree (FP-tree) is an efficient data structure for association-rule mining without generation of candidate itemsets. It was used to compress a database into a tree structure which stored only large items. It, however, needed to process all transactions in a batch way. In real-world applications, new transactions are usually incrementally inserted into databases. In the past, we proposed a Fast Updated FP-tree (FUFP-tree) structure to efficiently handle new transactions and to make the tree update process become easier. In this paper, we attempt to modify the FUFP-tree construction based on the concept of pre-large itemsets. Pre-large itemsets are defined by a lower support threshold and an upper support threshold. It does not need to rescan the original database until a number of new transactions have been inserted. The proposed approach can thus achieve a good execution time for tree construction especially when each time a small number of transactions are inserted. Experimental results also show that the proposed Pre-FUFP maintenance algorithm has a good performance for incrementally handling new transactions.

Original languageEnglish
Pages (from-to)9498-9505
Number of pages8
JournalExpert Systems With Applications
Volume36
Issue number5
DOIs
Publication statusPublished - 2009 Jul 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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